Software defect prediction method for open source software defect feature deep learning

A technology of software defect prediction and open source software, which is applied in the field of software engineering, can solve the problems that the prediction effect needs to be improved, and achieve good defect recognition effect and good effect

Active Publication Date: 2019-12-20
BEIHANG UNIV
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Problems solved by technology

However, the numerical sequence is a lossy transformation of the tree structure, and the defect information in the original

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  • Software defect prediction method for open source software defect feature deep learning
  • Software defect prediction method for open source software defect feature deep learning

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Embodiment Construction

[0018] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0019] The present invention proposes a software defect prediction method based on abstract syntax tree structural feature learning. First, the source code is expressed as an abstract syntax tree, and then the abstract syntax tree is cut by using repair information and community division to obtain defect subtrees, and repair The background information such as description and project information is integrated into the defect subtree for defect prediction. During the prediction, it is proposed to use the graph convolutional neural network to learn the effective expression of the defect subtree, and finally a better defect recognition effect can be obtained. The overall process is as follows figure 1 As shown, it mainly includes five steps,...

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Abstract

The invention provides a software defect prediction method for open source software defect feature deep learning, and belongs to the technical field of software engineering. The method comprises the steps of collecting open source software defect information, constructing a software defect database, and generating an abstract syntax tree from source codes; pruning the abstract syntax tree by usinga community detection algorithm to obtain a defect sub-tree, establishing an information corpus of the defect sub-tree in combination with the repair description, the project basic information and the source code, extracting theme words from the information corpus, converting the theme words into vector representation, and taking the vector representation as attributes of nodes in the defect sub-tree; finally, establishing a software defect prediction model of the convolutional neural network based on graph classification, expressing the defect subtree as an adjacent matrix and an attribute matrix to serve as input of the model to train the convolutional neural network, and recognizing whether the source code of the to-be-predicted software module has defect tendency or not. According tothe method, the defect depth features are directly extracted from the structured software codes by using a deep learning method, so that a better defect recognition effect can be achieved.

Description

technical field [0001] The invention belongs to the field of software engineering and relates to a software defect prediction method based on abstract syntax tree structure feature learning for open source software. Background technique [0002] With the growth of the scale and complexity of software systems, software defects are also increasing day by day. How to improve software quality and identify, predict, and repair software defects early has become a problem that must always be concerned and solved throughout the software life cycle. Software defect prediction can early identify modules with software defect tendencies that can affect software reliability based on software code characteristics and historical defect information, so as to make full use of effective resources to improve the quality and reliability of software products. [0003] The gradual maturity of machine learning technology makes data-driven software defect prediction based on statistical learning mo...

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Application Information

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IPC IPC(8): G06F11/36G06F8/41G06N3/04G06N3/08G06K9/62
CPCG06F11/3608G06F8/427G06N3/08G06N3/045G06F18/2132
Inventor 艾骏王飞许嘉熙郭皓然邹卓良施韬
Owner BEIHANG UNIV
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